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Establishing an adjusted p-value threshold to control the family-wide type 1 error in genome wide association studies

Overview of attention for article published in BMC Genomics, January 2008
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  • Above-average Attention Score compared to outputs of the same age and source (64th percentile)

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Citations

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106 Mendeley
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Title
Establishing an adjusted p-value threshold to control the family-wide type 1 error in genome wide association studies
Published in
BMC Genomics, January 2008
DOI 10.1186/1471-2164-9-516
Pubmed ID
Authors

Priya Duggal, Elizabeth M Gillanders, Taura N Holmes, Joan E Bailey-Wilson

Abstract

By assaying hundreds of thousands of single nucleotide polymorphisms, genome wide association studies (GWAS) allow for a powerful, unbiased review of the entire genome to localize common genetic variants that influence health and disease. Although it is widely recognized that some correction for multiple testing is necessary, in order to control the family-wide Type 1 Error in genetic association studies, it is not clear which method to utilize. One simple approach is to perform a Bonferroni correction using all n single nucleotide polymorphisms (SNPs) across the genome; however this approach is highly conservative and would "overcorrect" for SNPs that are not truly independent. Many SNPs fall within regions of strong linkage disequilibrium (LD) ("blocks") and should not be considered "independent". We proposed to approximate the number of "independent" SNPs by counting 1 SNP per LD block, plus all SNPs outside of blocks (interblock SNPs). We examined the effective number of independent SNPs for Genome Wide Association Study (GWAS) panels. In the CEPH Utah (CEU) population, by considering the interdependence of SNPs, we could reduce the total number of effective tests within the Affymetrix and Illumina SNP panels from 500,000 and 317,000 to 67,000 and 82,000 "independent" SNPs, respectively. For the Affymetrix 500 K and Illumina 317 K GWAS SNP panels we recommend using 10(-5), 10(-7) and 10(-8) and for the Phase II HapMap CEPH Utah and Yoruba populations we recommend using 10(-6), 10(-7) and 10(-9) as "suggestive", "significant" and "highly significant" p-value thresholds to properly control the family-wide Type 1 error. By approximating the effective number of independent SNPs across the genome we are able to 'correct' for a more accurate number of tests and therefore develop 'LD adjusted' Bonferroni corrected p-value thresholds that account for the interdepdendence of SNPs on well-utilized commercially available SNP "chips". These thresholds will serve as guides to researchers trying to decide which regions of the genome should be studied further.

Mendeley readers

The data shown below were compiled from readership statistics for 106 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 2 2%
United States 2 2%
Mexico 2 2%
Germany 1 <1%
Switzerland 1 <1%
Hong Kong 1 <1%
Netherlands 1 <1%
Unknown 96 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 32 30%
Student > Ph. D. Student 19 18%
Student > Master 15 14%
Professor 9 8%
Student > Bachelor 8 8%
Other 23 22%
Readers by discipline Count As %
Agricultural and Biological Sciences 55 52%
Biochemistry, Genetics and Molecular Biology 12 11%
Unspecified 12 11%
Medicine and Dentistry 8 8%
Computer Science 5 5%
Other 14 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 11 July 2014.
All research outputs
#1,956,535
of 5,123,382 outputs
Outputs from BMC Genomics
#1,790
of 4,656 outputs
Outputs of similar age
#29,066
of 92,238 outputs
Outputs of similar age from BMC Genomics
#31
of 95 outputs
Altmetric has tracked 5,123,382 research outputs across all sources so far. This one has received more attention than most of these and is in the 61st percentile.
So far Altmetric has tracked 4,656 research outputs from this source. They receive a mean Attention Score of 3.8. This one has gotten more attention than average, scoring higher than 59% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 92,238 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 67% of its contemporaries.
We're also able to compare this research output to 95 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 64% of its contemporaries.